Autonomous vehicle system for performing multi-sensor, multi-resolution fusion of object type and attribute information

ABSTRACT

A system receives an observation probability distribution function associated with a target object that was detected by sensors of an autonomous vehicle. The system identifies a target attribute of the target object, and detects a target attribute value associated with the target object. The system determines a first probability distribution function representing a probability of the autonomous vehicle detecting an object having an object label, determines a second probability distribution function defining a probability of the autonomous vehicle detecting the target attribute, determines a third probability distribution function defining a probability of the target attribute being present for the target object based on the target attribute value, and determines an attribute probability distribution function defining a probability that the target attribute is actually present for the target object. The system executes vehicle control instructions that cause the autonomous vehicle to adjust driving operations based on the attribute probability distribution function.

BACKGROUND

It is important to the operation of autonomous vehicles to be able topredict or forecast object behavior. Accurate forecasts of objectbehavior enables an autonomous vehicle to implement anticipatoryplanning and control rather than being reactive to its environment. Thisresults in a more natural driving behavior by the autonomous vehicle aswell as improved comfort and safety for its passengers.

Autonomous vehicles collect a large amount of data from numerous sensorsin order to perform object detection and object behavior prediction. Onesensor may collect data that is not available from other sensors. Forexample, a camera of an autonomous vehicle may be used to detect variousobject types associated with one or more objects, while LiDAR and radarare relatively limited in their ability to detect object type. On theother hand, cameras are typically limited in the depth information thatthey can capture. As such, a camera may be unable to consistentlydistinguish between a car advertisement on the side of a tractor trailerand an actual car on the road. However, LiDAR may clearly differentiatethe two by analyzing depth information.

Fusing information from multiple sources is a way to address thisproblem. But it is sometimes difficult for an autonomous vehicle todetermine which information from which sources should be aggregated andfused together to provide a characterization of a detected object.

This document describes methods and systems that are directed toaddressing the problems described above, and/or other issues.

SUMMARY

In various implementations, a system includes one or more electronicdevices; and a computer-readable storage medium. The computer-readablestorage medium includes one or more programming instructions that, whenexecuted, cause one or more of the one or more electronic devices toperform one or more actions. The system receives an observationprobability distribution function associated with a target object thatwas detected by one or more sensors of an autonomous vehicle. The one ormore sensors are associated with one or more object type labels. Theobservation probability distribution function includes a detectionlikelihood value associated with each of the one or more object typelabels. The system identifies a target attribute associated with thetarget object, and detects a target attribute value associated with thetarget object. The target attribute is indicative of whether the targetattribute was detected by the autonomous vehicle as being present ornot. The system determines a first probability distribution functionrepresenting a probability of the autonomous vehicle detecting an objecthaving one or more of the one or more object labels based on historicalobject type detections, determines a second probability distributionfunction defining a probability of the autonomous vehicle detecting thetarget attribute based on historical attribute detections, determines athird probability distribution function defining a probability of thetarget attribute actually being present for the target object based onthe target attribute value, and determines an attribute probabilitydistribution function defining a probability that the target attributeis actually present for the target object based on the first probabilitydistribution function, the second probability distribution function, andthe third probability distribution function. The attribute probabilitydistribution function includes a probability value associated with thetarget attribute being present for the target object, and a probabilityvalue associated with the target attribute not being present for thetarget object. The system executes one or more vehicle controlinstructions that cause the autonomous vehicle to adjust one or moredriving operations based on the attribute probability distributionfunction.

A system may determine a first probability distribution function byidentifying a prior probability distribution function associated with aset of one or more leaf nodes of a tree structure, where the priorprobability distribution is conditioned on observations received until acurrent time, transforming the observation probability distributionfunction to the set of leaf nodes to generate a transformationprobability distribution function, and fusing the transformationprobability distribution function with the prior probabilitydistribution to generate a posterior probability distribution function.

Transforming the observation probability distribution function to theleaf nodes may include, for each detection likelihood value of theobservation probability distribution function, dividing it equallyamongst the one or more leaf nodes that are its children.

Using the transformation probability distribution function with theprior probability distribution to generate a posterior probabilitydistribution function may include applying a Bayesian filter to thetransformation probability distribution function and the priorprobability distribution.

The second probability distribution function may include a probabilitydistribution function over an event that the target object is detectedas having the target attribute. The second probability distributionfunction may be conditioned on all observations made through a currenttime.

The system may determine a third probability distribution functionrepresenting a probability of the target attribute actually beingpresent for the target object based on the target attribute value bydetermining a third probability distribution that includes a set ofcombinations of attributes and object types, and for each combination,an associated likelihood value representing a likelihood of the objecttype of the combination having the attribute of the combination.

The system may determine an attribute probability value representing aprobability that the target attribute is actually present for the targetobject based on the first probability value, the second probabilityvalue, and the third probability value by summing a product value acrossthe target object label and the target attribute value. The productvalue may include a product of the first probability distributionfunction, the second probability distribution function, and the thirddistribution function.

The system may execute one or more vehicle control instructions thatcause the autonomous vehicle to adjust one or more driving operationsbased on the attribute probability distribution function by providingdata pertaining to the attribute probability distribution to one or moremachine learning models, receiving from the one or more machine learningmodels information pertaining to a predicted trajectory of the targetobject, and adjusting the one or more driving operations based on thepredicted trajectory of the target object.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example autonomous vehiclesystem.

FIG. 2 illustrates an example vehicle controller system.

FIG. 3 shows an example LiDAR system.

FIG. 4 illustrates a flow chart of an example method of estimating anobject type and attribute presence according to an embodiment.

FIG. 5 illustrates an example tree structure.

FIG. 6 illustrates a flow chart of an example method of fusing sensormeasurements from an autonomous vehicle.

FIG. 7 illustrates a flow chart of an example method of transforming aposterior pdf to one or more sample spaces.

FIG. 8 illustrates a flow chart of an example method of fusing sensormeasurements from an autonomous vehicle that have an attribute.

FIG. 9 illustrates an example compatibility constraint.

FIG. 10 illustrates an example relationship between object type andattribute compatibility.

FIG. 11 is a block diagram that illustrates various elements of apossible electronic system, subsystem, controller and/or other componentof an AV, and/or external electronic device.

DETAILED DESCRIPTION

As used in this document, the singular forms “a,” “an,” and “the”include plural references unless the context clearly dictates otherwise.Unless defined otherwise, all technical and scientific terms used hereinhave the same meanings as commonly understood by one of ordinary skillin the art. As used in this document, the term “comprising” means“including, but not limited to.” Definitions for additional terms thatare relevant to this document are included at the end of this DetailedDescription.

FIG. 1 is a block diagram illustrating an example system 100 thatincludes an autonomous vehicle 101 in communication with one or moredata stores 102 and/or one or more servers 103 via a network 110.Although there is one autonomous vehicle shown, multiple autonomousvehicles may be coupled to each other and/or coupled to data stores 102and/or servers 103 over network 110. Network 110 may be any type ofnetwork such as a local area network (LAN), a wide area network (WAN)such as the Internet, a cellular network, a satellite network, or acombination thereof, and may be wired or wireless. Data store(s) 102 maybe any kind of data stores such as, without limitation, map datastore(s), traffic information data store(s), user information datastore(s), point of interest data store(s), or any other type of contentdata store(s). Server(s) 103 may be any kind of servers or a cluster ofservers, such as, without limitation, Web or cloud servers, applicationservers, backend servers, or a combination thereof.

As illustrated in FIG. 1, the autonomous vehicle 101 may include asensor system 111, an on-board computing device 112, a communicationsinterface 114, and a user interface 115. Autonomous vehicle 101 mayfurther include certain components (as illustrated, for example, in FIG.2) included in vehicles, such as, an engine, wheels, steering wheel,transmission, etc., which may be controlled by the on-board computingdevice 112 using a variety of communication signals and/or commands,such as, for example, acceleration signals or commands, decelerationsignals or commands, steering signals or commands, braking signals orcommands, etc.

The sensor system 111 may include one or more sensors that are coupledto and/or are included within the autonomous vehicle 101. Examples ofsuch sensors include, without limitation, a LIDAR system, a radiodetection and ranging (RADAR) system, a laser detection and ranging(LADAR) system, a sound navigation and ranging (SONAR) system, one ormore cameras (e.g., visible spectrum cameras, infrared cameras, etc.),temperature sensors, position sensors (e.g., global positioning system(GPS), etc.), location sensors, fuel sensors, motion sensors (e.g.,inertial measurement units (IMU), etc.), humidity sensors, occupancysensors, or the like. The sensor data can include information thatdescribes the location of objects within the surrounding environment ofthe autonomous vehicle 101, information about the environment itself,information about the motion of the autonomous vehicle 101, informationabout a route of the autonomous vehicle, or the like. As autonomousvehicle 101 travels over a surface, at least some of the sensors maycollect data pertaining to the surface.

The LIDAR system may include a sensor configured to sense or detectobjects in an environment in which the autonomous vehicle 101 islocated. Generally, LIDAR system is a device that incorporates opticalremote sensing technology that can measure distance to a target and/orother properties of a target (e.g., a ground surface) by illuminatingthe target with light. As an example, the LIDAR system may include alaser source and/or laser scanner configured to emit laser pulses and adetector configured to receive reflections of the laser pulses. Forexample, the LIDAR system may include a laser range finder reflected bya rotating mirror, and the laser is scanned around a scene beingdigitized, in one, two, or more dimensions, gathering distancemeasurements at specified angle intervals. The LIDAR system, forexample, may be configured to emit laser pulses as a beam. Optionally,the beam may be scanned to generate two dimensional or three dimensionalrange matrices. In an example, the range matrices may be used todetermine distance to a given vehicle or surface by measuring time delaybetween transmission of a pulse and detection of a respective reflectedsignal. In some examples, more than one LIDAR system may be coupled tothe first vehicle to scan a complete 360° horizon of the first vehicle.The LIDAR system may be configured to provide to the computing device acloud of point data representing the surface(s), which have been hit bythe laser. The points may be represented by the LIDAR system in terms ofazimuth and elevation angles, in addition to range, which can beconverted to (X, Y, Z) point data relative to a local coordinate frameattached to the vehicle. Additionally, the LIDAR may be configured toprovide intensity values of the light or laser reflected off thesurfaces that may be indicative of a surface type. In examples, theLIDAR system may include components such as light (e.g., laser) source,scanner and optics, photo-detector and receiver electronics, andposition and navigation system. In an example, The LIDAR system may beconfigured to use ultraviolet (UV), visible, or infrared light to imageobjects and can be used with a wide range of targets, includingnon-metallic objects. In one example, a narrow laser beam can be used tomap physical features of an object with high resolution.

It should be noted that the LIDAR systems for collecting data pertainingto the surface may be included in systems other than the autonomousvehicle 101 such as, without limitation, other vehicles (autonomous ordriven), robots, satellites, etc.

FIG. 2 illustrates an example system architecture for a vehicle 201,such as the autonomous vehicle 101 of FIG. 1 autonomous vehicle. Thevehicle 201 may include an engine or motor 202 and various sensors formeasuring various parameters of the vehicle and/or its environment.Operational parameter sensors that are common to both types of vehiclesinclude, for example: a position sensor 236 such as an accelerometer,gyroscope and/or inertial measurement unit; a speed sensor 238; and anodometer sensor 240. The vehicle 101 also may have a clock 242 that thesystem architecture uses to determine vehicle time during operation. Theclock 242 may be encoded into the vehicle on-board computing device 212,it may be a separate device, or multiple clocks may be available.

The vehicle 201 also may include various sensors that operate to gatherinformation about the environment in which the vehicle is traveling.These sensors may include, for example: a location sensor 260 such as aGPS device; object detection sensors such as one or more cameras 262; aLIDAR sensor system 264; and/or a radar and or and/or a sonar system266. The sensors also may include environmental sensors 268 such as aprecipitation sensor and/or ambient temperature sensor. The objectdetection sensors may enable the vehicle 201 to detect objects that arewithin a given distance or range of the vehicle 201 in any direction,while the environmental sensors collect data about environmentalconditions within the vehicle's area of travel. The system architecturewill also include one or more cameras 262 for capturing images of theenvironment.

During operations, information is communicated from the sensors to anon-board computing device 212. The on-board computing device 212analyzes the data captured by the sensors and optionally controlsoperations of the vehicle based on results of the analysis. For example,the on-board computing device 212 may control braking via a brakecontroller 222; direction via a steering controller 224; speed andacceleration via a throttle controller 226 (in a gas-powered vehicle) ora motor speed controller 228 (such as a current level controller in anelectric vehicle); a differential gear controller 230 (in vehicles withtransmissions); and/or other controllers such as an auxiliary devicecontroller 254.

Geographic location information may be communicated from the locationsensor 260 to the on-board computing device 212, which may then access amap of the environment that corresponds to the location information todetermine known fixed features of the environment such as streets,buildings, stop signs and/or stop/go signals. Captured images from thecameras 262 and/or object detection information captured from sensorssuch as a LiDAR system 264 is communicated from those sensors) to theon-board computing device 212. The object detection information and/orcaptured images may be processed by the on-board computing device 212 todetect objects in proximity to the vehicle 201. In addition oralternatively, the vehicle 201 may transmit any of the data to a remoteserver system 103 (FIG. 1) for processing. Any known or to be knowntechnique for making an object detection based on sensor data and/orcaptured images can be used in the embodiments disclosed in thisdocument.

The on-board computing device 212 may obtain, retrieve, and/or createmap data that provides detailed information about the surroundingenvironment of the autonomous vehicle 201. The on-board computing device212 may also determine the location, orientation, pose, etc. of the AVin the environment (localization) based on, for example, threedimensional position data (e.g., data from a GPS), three dimensionalorientation data, predicted locations, or the like. For example, theon-board computing device 212 may receive GPS data to determine the AV'slatitude, longitude and/or altitude position. Other location sensors orsystems such as laser-based localization systems, inertial-aided GPS, orcamera-based localization may also be used to identify the location ofthe vehicle. The location of the vehicle may include an absolutegeographical location, such as latitude, longitude, and altitude as wellas relative location information, such as location relative to othercars immediately around it which can often be determined with less noisethan absolute geographical location. The map data can provideinformation regarding: the identity and location of different roadways,road segments, lane segments, buildings, or other items; the location,boundaries, and directions of traffic lanes (e.g., the location anddirection of a parking lane, a turning lane, a bicycle lane, or otherlanes within a particular roadway) and metadata associated with trafficlanes; traffic control data (e.g., the location and instructions ofsignage, traffic lights, or other traffic control devices); and/or anyother map data that provides information that assists the on-boardcomputing device 212 in analyzing the surrounding environment of theautonomous vehicle 201.

In certain embodiments, the map data may also include reference pathinformation that correspond to common patterns of vehicle travel alongone or more lanes such that the motion of the object is constrained tothe reference path (e.g., locations within traffic lanes on which anobject commonly travels). Such reference paths may be pre-defined suchas the centerline of the traffic lanes. Optionally, the reference pathmay be generated based on a historical observations of vehicles or otherobjects over a period of time (e.g., reference paths for straight linetravel, lane merge, a turn, or the like).

In certain embodiments, the on-board computing device 212 may alsoinclude and/or may receive information relating to the trip or route ofa user, real-time traffic information on the route, or the like.

The on-board computing device 212 may include and/or may be incommunication with a routing controller 231 that generates a navigationroute from a start position to a destination position for an autonomousvehicle. The routing controller 231 may access a map data store toidentify possible routes and road segments that a vehicle can travel onto get from the start position to the destination position. The routingcontroller 231 may score the possible routes and identify a preferredroute to reach the destination. For example, the routing controller 231may generate a navigation route that minimizes Euclidean distancetraveled or other cost function during the route, and may further accessthe traffic information and/or estimates that can affect an amount oftime it will take to travel on a particular route. Depending onimplementation, the routing controller 231 may generate one or moreroutes using various routing methods, such as Dijkstra's algorithm,Bellman-Ford algorithm, or other algorithms. The routing controller 231may also use the traffic information to generate a navigation route thatreflects expected conditions of the route (e.g., current day of the weekor current time of day, etc.), such that a route generated for travelduring rush-hour may differ from a route generated for travel late atnight. The routing controller 231 may also generate more than onenavigation route to a destination and send more than one of thesenavigation routes to a user for selection by the user from among variouspossible routes.

In various implementations, an on-board computing device 212 maydetermine perception information of the surrounding environment of theautonomous vehicle 201. Based on the sensor data provided by one or moresensors and location information that is obtained, the on-boardcomputing device 212 may determine perception information of thesurrounding environment of the autonomous vehicle 201. The perceptioninformation may represent what an ordinary driver would perceive in thesurrounding environment of a vehicle. The perception data may includeinformation relating to one or more objects in the environment of theautonomous vehicle 201. For example, the on-board computing device 212may process sensor data (e.g., LIDAR or RADAR data, camera images, etc.)in order to identify objects and/or features in the environment ofautonomous vehicle 201. The objects may include traffic signals, roadway boundaries, other vehicles, pedestrians, and/or obstacles, etc. Theon-board computing device 212 may use any now or hereafter known objectrecognition algorithms, video tracking algorithms, and computer visionalgorithms (e.g., track objects frame-to-frame iteratively over a numberof time periods) to determine the perception.

In some embodiments, the on-board computing device 212 may alsodetermine, for one or more identified objects in the environment, thecurrent state of the object. The state information may include, withoutlimitation, for each object: current location; current speed and/oracceleration, current heading; current pose; current shape, size, orfootprint; type (e.g., vehicle vs. pedestrian vs. bicycle vs. staticobject or obstacle); and/or other state information.

The on-board computing device 212 may perform one or more predictionand/or forecasting operations. For example, the on-board computingdevice 212 may predict future locations, trajectories, and/or actions ofone or more objects. For example, the on-board computing device 212 maypredict the future locations, trajectories, and/or actions of theobjects based at least in part on perception information (e.g., thestate data for each object comprising an estimated shape and posedetermined as discussed below), location information, sensor data,and/or any other data that describes the past and/or current state ofthe objects, the autonomous vehicle 201, the surrounding environment,and/or their relationship(s). For example, if an object is a vehicle andthe current driving environment includes an intersection, the on-boardcomputing device 212 may predict whether the object will likely movestraight forward or make a turn. If the perception data indicates thatthe intersection has no traffic light, the on-board computing device 212may also predict whether the vehicle may have to fully stop prior toenter the intersection.

In various embodiments, the on-board computing device 212 may determinea motion plan for the autonomous vehicle. For example, the on-boardcomputing device 212 may determine a motion plan for the autonomousvehicle based on the perception data and/or the prediction data.Specifically, given predictions about the future locations of proximateobjects and other perception data, the on-board computing device 212 candetermine a motion plan for the autonomous vehicle 201 that bestnavigates the autonomous vehicle relative to the objects at their futurelocations.

In one or more embodiments, the on-board computing device 212 mayreceive predictions and make a decision regarding how to handle objectsin the environment of the autonomous vehicle 201. For example, for aparticular object (e.g., a vehicle with a given speed, direction,turning angle, etc.), the on-board computing device 212 decides whetherto overtake, yield, stop, and/or pass based on, for example, trafficconditions, map data, state of the autonomous vehicle, etc. Furthermore,the on-board computing device 212 also plans a path for the autonomousvehicle 201 to travel on a given route, as well as driving parameters(e.g., distance, speed, and/or turning angle). That is, for a givenobject, the on-board computing device 212 decides what to do with theobject and determines how to do it. For example, for a given object, theon-board computing device 212 may decide to pass the object and maydetermine whether to pass on the left side or right side of the object(including motion parameters such as speed). The on-board computingdevice 212 may also assess the risk of a collision between a detectedobject and the autonomous vehicle 201. If the risk exceeds an acceptablethreshold, it may determine whether the collision can be avoided if theautonomous vehicle follows a defined vehicle trajectory and/orimplements one or more dynamically generated emergency maneuvers isperformed in a pre-defined time period (e.g., N milliseconds). If thecollision can be avoided, then the on-board computing device 212 mayexecute one or more control instructions to perform a cautious maneuver(e.g., mildly slow down, accelerate, change lane, or swerve). Incontrast, if the collision cannot be avoided, then the on-boardcomputing device 112 may execute one or more control instructions forexecution of an emergency maneuver (e.g., brake and/or change directionof travel).

As discussed above, planning and control data regarding the movement ofthe autonomous vehicle is generated for execution. The on-boardcomputing device 212 may, for example, control braking via a brakecontroller; direction via a steering controller; speed and accelerationvia a throttle controller (in a gas-powered vehicle) or a motor speedcontroller (such as a current level controller in an electric vehicle);a differential gear controller (in vehicles with transmissions); and/orother controllers.

In the various embodiments discussed in this document, the descriptionmay state that the vehicle or a controller included in the vehicle(e.g., in an on-board computing system) may implement programminginstructions that cause the vehicle and/or a controller to makedecisions and use the decisions to control operations of the vehicle.However, the embodiments are not limited to this arrangement, as invarious embodiments the analysis, decision making and or operationalcontrol may be handled in full or in part by other computing devicesthat are in electronic communication with the vehicle's on-boardcomputing device and/or vehicle control system. Examples of such othercomputing devices include an electronic device (such as a smartphone)associated with a person who is riding in the vehicle, as well as aremote server that is in electronic communication with the vehicle via awireless communication network. The processor of any such device mayperform the operations that will be discussed below.

Referring back to FIG. 1, the communications interface 114 may beconfigured to allow communication between autonomous vehicle 101 andexternal systems, such as, for example, external devices, sensors, othervehicles, servers, data stores, databases etc. Communications interface114 may utilize any now or hereafter known protocols, protectionschemes, encodings, formats, packaging, etc. such as, withoutlimitation, Wi-Fi, an infrared link, Bluetooth, etc. User interfacesystem 115 may be part of peripheral devices implemented within vehicle101 including, for example, a keyword, a touch screen display device, amicrophone, and a speaker, etc.

FIG. 3 shows an example LiDAR system 201 as may be used in variousembodiments. As shown in FIG. 3, the LiDAR system 201 includes a housing205 which may be rotatable 360° about a central axis such as hub or axle218. The housing may include an emitter/receiver aperture 211 made of amaterial transparent to light. Although the example shown in FIG. 3 hasa single aperture, in various embodiments, multiple apertures foremitting and/or receiving light may be provided. Either way, the systemcan emit light through one or more of the aperture(s) 211 and receivereflected light back toward one or more of the aperture(s) 211 as thehousing 205 rotates around the internal components. In an alternativeembodiment, the outer shell of housing 205 may be a stationary dome, atleast partially made of a material that is transparent to light, withrotatable components inside of the housing 205.

Inside the rotating shell or stationary dome is a light emitter system204 that is configured and positioned to generate and emit pulses oflight through the aperture 211 or through the transparent dome of thehousing 205 via one or more laser emitter chips or other light emittingdevices. The emitter system 204 may include any number of individualemitters, including for example 8 emitters, 64 emitters or 128 emitters.The emitters may emit light of substantially the same intensity, or ofvarying intensities. The individual beams emitted by 204 will have awell-defined state of polarization that is not the same across theentire array. As an example, some beams may have vertical polarizationand other beams may have horizontal polarization. The LiDAR system willalso include a light detector 208 containing a photodetector or array ofphotodetectors positioned and configured to receive light reflected backinto the system. The emitter system 204 and detector 208 would rotatewith the rotating shell, or they would rotate inside the stationary domeof the housing 205. One or more optical element structures 209 may bepositioned in front of the light emitting unit 204 and/or the detector208 to serve as one or more lenses or waveplates that focus and directlight that is passed through the optical element structure 209.

One or more optical element structures 309 may be positioned in front ofthe mirror 302 to focus and direct light that is passed through theoptical element structure 309. As shown below, the system includes anoptical element structure 309 positioned in front of the mirror 303 andconnected to the rotating elements of the system so that the opticalelement structure 309 rotates with the mirror 302. Alternatively or inaddition, the optical element structure 309 may include multiple suchstructures (for example lenses and/or waveplates). Optionally, multipleoptical element structures 309 may be arranged in an array on orintegral with the shell portion 311.

Optionally, each optical element structure 309 may include a beamsplitter that separates light that the system receives from light thatthe system generates. The beam splitter may include, for example, aquarter-wave or half-wave waveplate to perform the separation and ensurethat received light is directed to the receiver unit rather than to theemitter system (which could occur without such a waveplate as theemitted light and received light should exhibit the same or similarpolarizations).

The LiDAR system will include a power unit 321 to power the laseremitter unit 304, a motor 303, and electronic components. The LiDARsystem will also include an analyzer 315 with elements such as aprocessor 322 and non-transitory computer-readable memory 323 containingprogramming instructions that are configured to enable the system toreceive data collected by the light detector unit, analyze it to measurecharacteristics of the light received, and generate information that aconnected system can use to make decisions about operating in anenvironment from which the data was collected. Optionally, the analyzer315 may be integral with the LiDAR system 301 as shown, or some or allof it may be external to the LiDAR system and communicatively connectedto the LiDAR system via a wired or wireless communication network orlink.

The present disclosure generally describes a scalable and extensibleprobabilistic framework for fusing disparate information from multipleautonomous vehicle sensors into a single estimation of object type andattribute for an object. The consideration of multiple sensors improvesoverall accuracy by leveraging the strengths of the sensors andproviding multi-sensor redundancy.

FIG. 4 illustrates a flow chart of an example method of estimating anobject type and attribute presence according to an embodiment. Asillustrated by FIG. 4, an observation may be received 400. Theobservation may be received from one or more sensors of a perceptionsubsystem of an autonomous vehicle, and the observation may correspondto an object that was observed by the autonomous vehicle. Theobservation may include an estimated object type associated with theobject and an attribute value associated with one or more attributes.The attribute value may be binary indicating whether the attribute wasdetected as being present. Attributes will be discussed later in thisdisclosure, but an example of an attribute is whether an object isarticulated (e.g., a tractor trailer or bus). For example, an attributevalue of ‘0’ for an ‘articulated’ attribute may indicate that the objectwas not detected as being articulated, while an attribute value of ‘1’for the attribute may indicate that the object was detected as beingarticulated.

Referring back to FIG. 4, the system may determine 402 a firstprobability distribution function representing a probability of anautonomous vehicle detecting an object having the object label of theobservation. This determination may be based on historical object typedetections. In various embodiments, these historical object typedetections may be organized in a tree structure as discussed below, andmay be used to estimate an object type and/or attribute for anobservation.

FIG. 5 illustrates an example of a tree structure according to anembodiment. As illustrated by FIG. 5, a tree may have one or more samplespaces. A sample space refers to a set of one or more object type labelsthat are measurable by one or more sensors of an autonomous vehicle andrepresent a class of all possible outcomes associated with the sensor.In terms of a tree structure, an object type label may be referred to inthis disclosure as a “node”.

For example, if the sensor is a radar sensor, a class associated withthe sensor may be {fast, slow, static} where “fast”, “slow”, and“static” are each object type labels. These three outcomes may representall possible outcomes for a radar sensor. In other words, data collectedby a radar sensor may correspond to only a fast object, a slow object,or a static object.

FIG. 5 illustrates three example sample spaces {L, P, Q}. Sample space Pmay represent data that is collected from one or more LiDAR sensors. Forexample, data that is a part of sample space P may be indicative ofobject type associated with one or more observations. For instance, inFIGS. 4, P1, P2, P3, and P4, each represent a object type label insample space P, where P1 includes data associated with objectsdetermined to be vehicles, P2 includes data associated with objectsdetermined to be bicycles, P3 includes data associated with objectsdetermined to be pedestrians, and P4 includes data associated withobjects determined to be part of the background. Objects in thebackground may include, without limitation, road cones, bollards,signage, and/or the like. Additional and/or alternative data or types ofdata may be used within the scope of this disclosure.

Sample space Q may represent data that is collected from one or moreradar sensors. For example, data that is part of sample space Q may beindicative of motion or movement of one or more observations. Forinstance, in FIG. 5, Q1, Q2, and Q3 each represent an object type labelin sample space Q, where Q1 includes data associated with observationsdetermined to be fast objects, Q2 includes data associated withobservations determined to be slow objects, and Q3 includes dataassociated with observations determined to be static objects. Additionaland/or alternative data or types of data may be used within the scope ofthis disclosure.

Sample space L may represent a minimum set of object types that areneeded to support one or more tree structures. For instance, in FIG. 5,{L1, . . . , L6} each represent an object type label of sample space L,where:

L1 includes data associated with observations determined to be smallvehicles

L2 includes data associated with observations determined to be largevehicles

L3 includes data associated with observations determined to be bicycles

L4 includes data associated with observations determined to bemotorcycles

L5 includes data associated with observations determined to bepedestrians

L6 includes data associated with observations determined to bebackground

In various embodiments, for each tree, each child node may connect toonly one parent such that there is only one path from any leaf label tothe root of the tree.

Within each sample space, each node or label must be mutually exclusive.This may be represented by: p(

, |

,)=0∀{∈,

, where 1 represents a node or label, and S represents a sample space.

Each sample space may be exhaustive. This means that the object typelabels included in each sample space must represent the exhaustiveoptions associated with the sample space, and may be represented by:

${\sum\limits_{i \in S}{p\left( \ell_{i} \right)}} = 1$

In various embodiments, at a particular point in time, a probabilitydistribution function (also referred to in this disclosure as a “pdf”)may exist over one or more sample spaces. A probability distributionfunction may have a detection likelihood value that is associated witheach object type label of the sample space. A detection likelihood valuerepresents a probability that an observation corresponds to that objecttype label. A probability distribution function may be conditioned onall observations up through a previous time step.

For example, referring back to FIG. 5, a probability distributionfunction over sample space L (the leaf labels) through time step k−1 maybe represented as:

P(L|z _(1:k−1))=[0.12,0.18,0.50,0.07,0.03,0.10]

From this probability distribution function, it can be discerned that,based on past observations, an object is most likely to be a bicycle, asthe object type label L3 is associated with a detection likelihood valuehaving the highest value (e.g., 0.50).

In various embodiments, a classifier (i.e., a trained model) maygenerate one or more of the detection likelihood values. A classifiermay be associated with a particular sensor or sensor type. For instance,a radar sensor may be associated with a first classifier that receivesdata pertaining to an observation captured by the radar sensor anddetermining one or more detection likelihood values associated with theobservation. The detection likelihood values may each correspond to alabel associated with the sensor. For example, the radar sensor may beassociated with the labels {fast, slow, static}. As such, the classifierassociated with the radar sensor may for an observation determine adetection likelihood value for each of the label values. For example,the classifier may determine the detection likelihood values {0.20,0.70, 0.10}, meaning that there is a 20% likelihood that the observationis a fast object, a 70% likelihood that the observation is a slowobject, and a 10% likelihood that the observation is a static object.

In various embodiments, a classifier may learn and predict a probabilitydistribution over a class. In other embodiments, a classifier may learnand predict a probability distribution over a class and then encode theprobability distribution using a confusion matrix. A confusion matrixmay include information about past predictions made by the classifierand the number of times that such predictions were actually correction.For example, a confusion matrix may include information that of the ‘n’times the classifier predicted an observation was a VEHICLE, it wascorrect m<n times, that the object was actually a PEDESTRIAN k times,and/or the like. The encoded probability distribution may be normalized.

FIG. 6 illustrates a flow chart of an example method of fusing sensormeasurements from an autonomous vehicle according to an embodiment. Asillustrated in FIG. 6, a system may identify 600 a prior probabilitydistribution function across a sample space associated with leaf nodesof a tree structure (referred to throughout this disclosure as the “leafsample space”). In various embodiments, a sensor subsystem 111 of anautonomous vehicle may identify a prior probability distributionfunction. However, it is understood that other subsystems of anautonomous vehicle or autonomous vehicle system may perform this step.

The prior probability distribution function may be conditioned on allobservations collected by the system up through time step k−1. Thefollowing example prior probability distribution function will bereferred to throughout the discussion of FIG. 6:

Prior pdf=P(L|z _(1:k−1))=[0.12,0.18,0.50,0.07,0.03,0.10]

The system may receive 602 an observation probability distributionfunction across another sample space at time step k from a classifierassociated with a sensor of an autonomous vehicle. For example, aperception subsystem 122 may receive 602 an observation probabilitydistribution function that is based on measurements obtained by a sensorsubsystem. Referring to the above example, the system may receive thefollowing example observation distribution function across the P samplespace from FIG. 5 from a classifier of a sensor:

Observation pdf=P(L|z _(1:k−1))=[0.20,0.50,0.20,0.10]

The system (e.g., a perception subsystem 122) may transform 604 thereceived observation probability distribution function to one or moresample spaces of its children. The system may transform 604 the receivedobservation probability distribution function to the lead sample spaceby, for each object type label or label in the observation probabilitydistribution function, dividing its detection likelihood value equallyover its child nodes. For example, referring to the example observationdistribution function above, object type label P1 may be associated witha detection likelihood value of 0.20. The system may divide this valueof 0.20 by two since node P1 has two child nodes.

The system may generate 606 a transformation probability distributionfunction associated with the transformation. The system may generate 606a transformation probability distribution function by dividing eachdetection likelihood value from the observation distribution function bythe number of its associated child nodes, and adding that value to thetransformation probability distribution function for each child node.The following illustrates an example transformation probabilitydistribution function associated with the example observationprobability distribution function referred to above:

Transformation pdf=P(z _(k) |L)=[0.10,0.10,0.25,0.25,0.20,0.10]

As illustrated from the transformation pdf, an observation is mostlikely to be a bicycle (object type label L3 with a detection likelihoodvalue of 0.25) or a motorcycle (object type label L4 with a detectionlikelihood value of 0.25).

The system may fuse 608 the transformation probability distributionfunction with the prior probability distribution function to generate aposterior probability distribution function. The posterior probabilitydistribution function may be the result of fusing the observation attime k with the prior observations through time k−1. The system mayperform this fusion using a Bayesian filter. For example, a Bayesianfilter may be applied as follows:

${{Posterior}\mspace{14mu}{pdf}} = \frac{{transformation}\mspace{14mu}{pdf}*{prior}\mspace{14mu}{pdf}}{\left( {{sum}\mspace{14mu}{over}\mspace{14mu} L} \right)*\left( {{transformation}\mspace{14mu}{pdf}} \right)*\left( {{prior}\mspace{14mu}{pdf}} \right)}$

The posterior pdf for the above example can be represented as:

Posterior pdf=p(L|z _(1:k))=[0.06,0.10,0.66,0.09,0.04,0.05]

As illustrated by the example posterior pdf, the detection likelihoodvalue associated with L3 (0.66) increased from its value from the priorpdf (0.50), indicating that there is greater confidence that anobservation is a bicycle under the posterior pdf than the prior pdf.

The posterior pdf may reflect the current prediction state until a newobservation pdf is received from one or more sensors of an autonomousvehicle. When this occurs, steps 602-608 may be repeated.

In various embodiments, a posterior pdf may be transformed to one ormore sample spaces. A posterior pdf may be transformed to a sample spacein order to ascertain how a posterior pdf may affect one or moredetection likelihood values of one or more other sample spaces. Forinstance, referring to the example above, the posterior pdf isdetermined in response to receiving an observation pdf pertaining tosample space P. But the effect of the posterior pdf on sample space Qmay need to be determined.

FIG. 7 illustrates a flow chart of an example method of transforming aposterior pdf to one or more sample spaces according to an embodiment.The process illustrated by FIG. 7 may be performed by a subsystem of anautonomous vehicle system (e.g., a forecasting and prediction subsystem123) querying a tree structure to ascertain one or more updateddetection likelihood values of a sample space as a result of theposterior pdf. As another example, a system user may query a treestructure to ascertain one or more updated detection likelihood valuesof a sample space as a result of the posterior pdf.

As illustrated by FIG. 7, a posterior pdf may be transformed to a samplespace by summing the detection likelihood values associated with itschildren. The system may identify 700 a node of the sample space that isto be queried. The system may identify 702, using a tree structure, thechildren nodes associated with the identified node. For each identifiedchild node, the system may determine 704 the detection likelihood valueassociated with the child node from the posterior pdf. The system maysum 706 the determined detection likelihood values to determine anupdated detection likelihood value for the identified node. This processmay be repeated for other nodes in the queried sample space.

For example, referring back to the above example, a user may want toquery sample space Q in light of the posterior probability distributionfunction p (L|z1:1)=[0.06, 0.10, 0.66, 0.09, 0.04, 0.05]

Referring back to FIG. 5, it is seen that object type label Q1 haschildren L1, L2, and L4. An updated detection likelihood value can bedetermined for Q1 by summing the detection likelihood values associatedwith L1, L2 and L4 from the posterior pdf. As such, the updateddetection likelihood value for Q1=0.06+010+0.09=0.25. The followingrepresents the result of transforming the posterior pdf to the entiresample space Q:

p(Q|z _(1:k))=[0.25,0.7,0.05]

As illustrated by this pdf over the Q sample space, it is more likelythat an observation will be a slow moving object (Q2) based on this pdf.

Referring back to FIG. 4, the system may determine 404 a secondprobability distribution value representing a probability of anautonomous vehicle detecting the attribute of the observation based onhistorical attribute detections. Orthogonal binary attributes may bemodeled to supplement one or more object type labels. An orthogonalbinary attribute (also referred to in this disclosure as “attribute”)refers to a descriptor of an object type label that is either true orfalse. An attribute may be static, meaning that its state is alwaysactive or never active. An attribute may be dynamic, meaning that itsstate can transition from active to inactive.

An example attribute may be “EMERGENCY”. Rather than adding uniqueobject type labels for each emergency-related object that an autonomousvehicle may encounter, object type labels may be modeled as having anEMERGENCY attribute. For instance, a police car may be modeled as aVEHICLE object type with an EMERGENCY attribute. A police motorcycle maybe modeled as a MOTORCYCLE object type with an EMERGENCY attribute.Similarly, a police officer may be modeled as a PEDESTRIAN object typewith an EMERGENCY attribute.

Additional and/or alternate object types and/or attributes may be usedwithin the scope of this disclosure.

As another example, object type labels may be modeled as having anARTICULATED attribute. Rather than adding unique object type labels foreach articulated object that an autonomous vehicle may encounter, objecttype labels may be modeled as having an ARTICULATED attribute. Forinstance, a tractor trailer may be modeled as a LARGE_VEHICLE objecttype with an ARTICULATED attribute. An articulated bus may be modeled asa CITY_BUS object type with an ARTICULATED attribute. Additional and/oralternate object types and/or attributes may be used within the scope ofthis disclosure.

Attributes may be modeled as mutually independent such that:

p(d _(i) |d _(J))=p(d _(i) )∀i ≠J

Where d_(i) and d_(j) represent two different attributes.

The probability that an attribute is observed given a sequence ofattribute detections may be estimated using parallel binary Bayesfilters (BBF), one per attribute. For a given track and attribute, let:

a∈{F,T}=The event that the attribute is present

d∈{F,T}=The event that the attribute is observed

∈{1, . . . ,N}=The object type label

z _(1:k)=History of object type detections

y _(1:k)=History of attribute detections

Referring back to FIG. 4, the system may determine 406 a thirdprobability distribution function representing a probability of theattribute from the observation actually being present for the objectbased on its attribute value. Object type and attribute compatibilitymay be considered as part of this determination. The system maydetermine 408 an attribute probability distribution functionrepresenting a probability that the observed attribute is actuallypresent for the observed object based on the first probabilitydistribution function, the second probability distribution function, andthe third probability distribution function.

The attribute probability distribution function may be considered asfollows:

$\begin{matrix}{{p\left( {{a_{k}❘z_{1:k}},y_{1:k}} \right)} = {\sum\limits_{\ell_{k}}{\sum\limits_{d_{k}}{p\left( {\ell_{k},d_{k},{a_{k}❘z_{1:k}},y_{1:k}} \right)}}}} \\{= {\sum\limits_{\ell_{k}}{\sum\limits_{d_{k}}{{p\left( {\ell_{k}❘z_{1:k}} \right)}{p\left( {d_{k}❘y_{1:k}} \right)}{p\left( {{a_{k}❘\ell_{k}},d_{k}} \right)}}}}}\end{matrix}$

Where:

p(

_(k) |z _(1:k)) represents a posterior pdf

p(d _(k) |y _(1:k)) represents a posterior BBF

p(a _(k) |d _(k),

_(k)) represents a set of compatibility constraints

The compatibility description above may be based on the followingassumptions:

(p(

_(k) |z _(1:k) ,y _(1:k))=p(

_(k) |z _(1:k))

(p(d _(k)|

_(k) ,z _(1:k) ,y _(1:k))=p(d _(k) |y _(1:k))

(p(a _(k) |d _(k),

_(k) ,z _(1:k) ,y _(1:k))=p(a _(k) |d _(k),

_(k))

In various embodiments, the set of compatibility constraints may be oneor more probabilistic object type attribute compatibility constraintsthat are encoded as probabilities. These constraints may defined rulesor constraints about a relationship between an object type and anattribute. An example compatibility constraint may be that a BICYCLEobject type will not have an ARTICULATED attribute.

In various embodiments, one or more of the compatibility constraints maybe considered “soft constraints” meaning that they do not dictate whichobject types and attributes can or cannot be used together. Rather, theyprovide insight into which object types and attributes are likely orunlikely to be used together. In some embodiments, one or more of thecompatibility constraints may be considered “hard constraints” meaningthat they do require a certain object type and attribute to not be usedtogether. A hard constraint may have a probability value of zero.

FIG. 8 illustrates a flow chart of an example method of fusing sensormeasurements from an autonomous vehicle that have an attribute accordingto an embodiment. As illustrated in FIG. 8, a system may identify 800 aprior probability distribution function over an event that an objectthat is detected has a particular attribute. The prior probabilitydistribution function may be conditioned on all observations collectedby the system up through time step k−1. The following example priorprobability distribution function will be referred to throughout thediscussion of FIG. 8:

Prior pdf=P(d|y _(1:k−1))=[0.40,0.60]

Because an attribute is either present or not, a probabilitydistribution function will have two possible values—a probability thatthe attribute is not present, and a probability that the attribute ispresent. For example, referring to the above example, if the applicableattribute is ARTICULATED, then 0.40 represents the probability that anobject is not articulated, and 0.60 represents the probability that anobject is articulated. Additional and/or alternate probabilities may beused within the scope of this disclosure.

The system may receive 802 an observation probability distributionfunction over an event at time step k from a classifier associated witha sensor of an autonomous vehicle. For example, a perception subsystem122 may receive 802 an observation probability distribution functionthat is based on measurements obtained by a sensor subsystem. Referringto the above example, the system may receive 802 the following exampleobservation distribution function from a classifier of a sensor:

Observation pdf=p(y _(k) |d)=[0.15,0.85]

As such, the observation pdf may indicate that there is a probability of0.15 over an observation that an object is not articulated, and a 0.85probability over the observation that the object is articulated.

The system (e.g., a perception subsystem 122) may fuse 804 theobservation probability distribution function with the prior probabilitydistribution function to generate a posterior probability distributionfunction. The posterior probability distribution function may be theresult of fusing the observation at time k with the prior observationsthrough time k−1. The system may perform this fusion using a Bayesianfilter. For example, a Bayesian filter may be applied as follows:

${{Posterior}\mspace{14mu}{pdf}} = \frac{{observation}\mspace{14mu}{pdf}*{prior}\mspace{14mu}{pdf}}{\left( {{sum}\mspace{14mu}{over}\mspace{14mu} d} \right)*\left( {{observation}\mspace{14mu}{pdf}} \right)*\left( {{prior}\mspace{14mu}{pdf}} \right)}$

The posterior pdf for the above example can be represented as:

Posterior pdf=p(d|y _(1:k))=[0.10,0.90]

In various implementations, an additional step may be needed for dynamicattributes. This step may involve determining the prior probabilitydistribution function at current time, k, from the posterior probabilitydistribution at the previous time step, k−1. This may be accomplishedthrough the use of a transition density probability distributionfunction. A transition probability distribution function may encode theprobability of transitioning from one state to another. For example, atransition probability function may encode the probability oftransitioning from a state where an attribute is present to a statewhere the attribute is not present, or the probability of transitioningfrom a state where the attribute is not present to a state where anattribute is present.

As discussed above, compatibility constraints may be defined rules abouta relationship between an object type and an attribute. Thisrelationship may be specific to one or more sample spaces. Compatibilityconstraints may be learned from historic data or offline data, and maybe provided to the system. FIG. 9 illustrates an example compatibilityconstraint pertaining to sample space Q (from FIG. 5) according tovarious embodiments.

FIG. 9 illustrates example compatibility constraints according tovarious embodiments. As illustrated by FIG. 9, two different probabilitydistributions across sample space Q are considered. The first 900represents a probability distribution that an object is not detected ashaving an attribute, in this example, that attribute is beingarticulated. The second probability distribution 902 represents alikelihood that an object is detected as having the attribute (e.g.,being articulated). The left column for each distribution represents aprobability that an object actually has the attribute (e.g., actually isarticulated), while the right column for each distribution represents aprobability that an object does not actually have the attribute (e.g.,is not articulated).

A compatibility constraint may exist that a slow moving object (e.g., abicycle or a pedestrian) is unlikely to be articulated even if they aredetected as such. For example, as illustrated by FIG. 9, there is a 0.1probability that a slow object that is detected as not being articulatedin the Q sample space is actually not articulated. And there is a 0.2probability that a slow object that is detected as being articulated inthe Q sample space is actually not articulated.

However, by applying the compatibility constraint, the system may use aprobability distribution function that represents the fact that slowmoving objects are unlikely to be articulated even if they are detectedas such.

As discussed above, object type and attribute compatibility may beconsidered based on the following relationship:

$\begin{matrix}{{p\left( {{a_{k}❘z_{1:k}},y_{1:k}} \right)} = {\sum\limits_{\ell_{k}}{\sum\limits_{d_{k}}{p\left( {\ell_{k},d_{k},{a_{k}❘z_{1:k}},y_{1:k}} \right)}}}} \\{= {\sum\limits_{\ell_{k}}{\sum\limits_{d_{k}}{{p\left( {\ell_{k}❘y_{1:k}} \right)}{p\left( {d_{k}❘z_{1:k}} \right)}{p\left( {{a_{k}❘\ell_{k}},d_{k}} \right)}}}}}\end{matrix}$

FIG. 10 illustrates an example relationship between object type andattribute compatibility for the above example pertaining to sample spaceQ. FIG. 10 also illustrates a detailed explanation of how therelationship is applied with specific example values. As shown by FIG.10, despite there being a high probability that articulation wasdetected, the soft constraint that slow moving objects are unlikely tobe articulated results in the object being unlikely to actually bearticulated.

Referring back to FIG. 4, the system may execute 410 one or more vehiclecontrol instructions. The one or more vehicle control instructions maycause an autonomous vehicle to adjust one or more of its drivingoperations based on the determined attribute probability distributionfunction. For example, one or more vehicle control instructions maycause an autonomous vehicle to adjust one or more of its drivingoperations based on a determined attribute probability distributionfunction when it encounters an object associated with the determinedattribute probability distribution.

As an example, an autonomous vehicle may use a determined attributeprobability distribution of an object to predict a state of the objectforward in time and adjust operations of the autonomous vehicleaccordingly. For instance, an autonomous vehicle may predict that anobject having a VEHICLE object type is likely to follow road lanes andobey the rules of the road. The autonomous vehicle may predict that anobject having a VEHICLE object type with an ARTICULATED attribute may bemore likely to take a wide turn. As such, an autonomous vehicle may notpass such an object in proximity to an intersection.

As another example, an autonomous vehicle may pull over or stop when itdetects an object having an EMERGENCY attribute such as, for example, apolice vehicle, an ambulance, a fire truck, and/or the like.

In various implementations, the determined object type and/orattribute(s) associated with an object may be provided to one or moremachine learning models. A “machine learning model” or a “model” refersto a set of algorithmic routines and parameters that can predict anoutput(s) of a real-world process (e.g., prediction of an objecttrajectory or behavior) based on a set of input features, without beingexplicitly programmed. A structure of the software routines (e.g.,number of subroutines and relation between them) and/or the values ofthe parameters can be determined in a training process, which can useactual results of the real-world process that is being modeled. Suchsystems or models are understood to be necessarily rooted in computertechnology, and in fact, cannot be implemented or even exist in theabsence of computing technology. While machine learning systems utilizevarious types of statistical analyses, machine learning systems aredistinguished from statistical analyses by virtue of the ability tolearn without explicit programming and being rooted in computertechnology.

The one or more machine learning models may generate data pertaining toa predicted trajectory or predicted behavior for an object. This datamay be used by the autonomous vehicle to generate one or more vehiclecontrol instructions that cause an autonomous vehicle to adjust one ormore of its driving operations. For example, passing an object type ofVEHICLE and an ATTRIBUTE type of EMERGENCY to one or more machinelearning models may yield data indicating that the predicted trajectoryof the object will be to pass the autonomous vehicle on the left at ahigh rate of speed. In response, the autonomous vehicle may use thisdata to generate one or more vehicle control instructions that cause theautonomous vehicle to apply the brakes and reduce its speed as theobject approaches. Alternatively, the autonomous vehicle may use thisdata to generate one or more vehicle control instructions that cause theautonomous vehicle to navigate to a right-hand shoulder and stop itsmovement until the object passes. Additional and/or alternate objecttypes, attributes, and/or actions may be used within the scope of thisdisclosure.

FIG. 11 depicts an example of internal hardware that may be included inany of the electronic components of the system, such as internalprocessing systems of the AV, external monitoring and reporting systems,or remote servers. An electrical bus 1100 serves as an informationhighway interconnecting the other illustrated components of thehardware. Processor 1105 is a central processing device of the system,configured to perform calculations and logic operations required toexecute programming instructions. As used in this document and in theclaims, the terms “processor” and “processing device” may refer to asingle processor or any number of processors in a set of processors thatcollectively perform a set of operations, such as a central processingunit (CPU), a graphics processing unit (GPU), a remote server, or acombination of these. Read only memory (ROM), random access memory(RAM), flash memory, hard drives and other devices capable of storingelectronic data constitute examples of memory devices 1125. A memorydevice may include a single device or a collection of devices acrosswhich data and/or instructions are stored. Various embodiments of theinvention may include a computer-readable medium containing programminginstructions that are configured to cause one or more processors toperform the functions described in the context of the previous figures.

An optional display interface 1130 may permit information from the bus1100 to be displayed on a display device 1135 in visual, graphic oralphanumeric format, such on an in-dashboard display system of thevehicle. An audio interface and audio output (such as a speaker) alsomay be provided. Communication with external devices may occur usingvarious communication devices 1140 such as a wireless antenna, a radiofrequency identification (RFID) tag and/or short-range or near-fieldcommunication transceiver, each of which may optionally communicativelyconnect with other components of the device via one or morecommunication system. The communication device(s) 1140 may be configuredto be communicatively connected to a communications network, such as theInternet, a local area network or a cellular telephone data network.

The hardware may also include a user interface sensor 1145 that allowsfor receipt of data from input devices 1150 such as a keyboard orkeypad, a joystick, a touchscreen, a touch pad, a remote control, apointing device and/or microphone. Digital image frames also may bereceived from a camera 1120 that can capture video and/or still images.The system also may receive data from a motion and/or position sensor1170 such as an accelerometer, gyroscope or inertial measurement unit.The system also may receive data from a LiDAR system 1160 such as thatdescribed earlier in this document.

The above-disclosed features and functions, as well as alternatives, maybe combined into many other different systems or applications. Variouscomponents may be implemented in hardware or software or embeddedsoftware. Various presently unforeseen or unanticipated alternatives,modifications, variations or improvements may be made by those skilledin the art, each of which is also intended to be encompassed by thedisclosed embodiments.

Terminology that is relevant to the disclosure provided above includes:

An “automated device” or “robotic device” refers to an electronic devicethat includes a processor, programming instructions, and one or morecomponents that based on commands from the processor can perform atleast some operations or tasks with minimal or no human intervention.For example, an automated device may perform one or more automaticfunctions or function sets. Examples of such operations, functions ortasks may include without, limitation, navigation, transportation,driving, delivering, loading, unloading, medical-related processes,construction-related processes, and/or the like. Example automateddevices may include, without limitation, autonomous vehicles, drones andother autonomous robotic devices.

The term “classifier” means an automated process by which an artificialintelligence system may assign a label or category to one or more datapoints. A classifier includes an algorithm that is trained via anautomated process such as machine learning. A classifier typicallystarts with a set of labeled or unlabeled training data and applies oneor more algorithms to detect one or more features and/or patterns withindata that correspond to various labels or classes. The algorithms mayinclude, without limitation, those as simple as decision trees, ascomplex as Naïve Bayes classification, and/or intermediate algorithmssuch as k-nearest neighbor. Classifiers may include artificial neuralnetworks (ANNs), support vector machine classifiers, and/or any of ahost of different types of classifiers. Once trained, the classifier maythen classify new data points using the knowledge base that it learnedduring training. The process of training a classifier can evolve overtime, as classifiers may be periodically trained on updated data, andthey may learn from being provided information about data that they mayhave mis-classified. A classifier will be implemented by a processorexecuting programming instructions, and it may operate on large datasets such as image data, LIDAR system data, sensor data, and/or otherdata.

The term “vehicle” refers to any moving form of conveyance that iscapable of carrying either one or more human occupants and/or cargo andis powered by any form of energy. The term “vehicle” includes, but isnot limited to, cars, trucks, vans, trains, autonomous vehicles,aircraft, aerial drones and the like. An “autonomous vehicle” is avehicle having a processor, programming instructions and drivetraincomponents that are controllable by the processor without requiring ahuman operator. An autonomous vehicle may be fully autonomous in that itdoes not require a human operator for most or all driving conditions andfunctions, or it may be semi-autonomous in that a human operator may berequired in certain conditions or for certain operations, or that ahuman operator may override the vehicle's autonomous system and may takecontrol of the vehicle. Autonomous vehicles also include vehicles inwhich autonomous systems augment human operation of the vehicle, such asvehicles with driver-assisted steering, speed control, braking, parkingand other systems.

In this document, the terms “street,” “lane” and “intersection” areillustrated by way of example with vehicles traveling on one or moreroads. However, the embodiments are intended to include lanes andintersections in other locations, such as parking areas. In addition,for autonomous vehicles that are designed to be used indoors (such asautomated picking devices in warehouses), a street may be a corridor ofthe warehouse and a lane may be a portion of the corridor. If theautonomous vehicle is a drone or other aircraft, the term “street” mayrepresent an airway and a lane may be a portion of the airway. If theautonomous vehicle is a watercraft, then the term “street” may representa waterway and a lane may be a portion of the waterway.

As used in this document, the term “light” means electromagneticradiation associated with optical frequencies, e.g., ultraviolet,visible, infrared and terahertz radiation. Example emitters of lightinclude laser emitters and other emitters that emit converged light. Inthis document, the term “emitter” will be used to refer to an emitter oflight, such as a laser emitter that emits infrared light.

An “electronic device” or a “computing device” refers to a device thatincludes a processor and memory. Each device may have its own processorand/or memory, or the processor and/or memory may be shared with otherdevices as in a virtual machine or container arrangement. The memorywill contain or receive programming instructions that, when executed bythe processor, cause the electronic device to perform one or moreoperations according to the programming instructions.

The terms “memory,” “memory device,” “data store,” “data storagefacility” and the like each refer to a non-transitory device on whichcomputer-readable data, programming instructions or both are stored.Except where specifically stated otherwise, the terms “memory,” “memorydevice,” “data store,” “data storage facility” and the like are intendedto include single device embodiments, embodiments in which multiplememory devices together or collectively store a set of data orinstructions, as well as individual sectors within such devices.

The terms “processor” and “processing device” refer to a hardwarecomponent of an electronic device that is configured to executeprogramming instructions. Except where specifically stated otherwise,the singular term “processor” or “processing device” is intended toinclude both single-processing device embodiments and embodiments inwhich multiple processing devices together or collectively perform aprocess.

In this document, the terms “communication link” and “communicationpath” mean a wired or wireless path via which a first device sendscommunication signals to and/or receives communication signals from oneor more other devices. Devices are “communicatively connected” if thedevices are able to send and/or receive data via a communication link.“Electronic communication” refers to the transmission of data via one ormore signals between two or more electronic devices, whether through awired or wireless network, and whether directly or indirectly via one ormore intermediary devices.

In this document, when relative terms of order such as “first” and“second” are used to modify a noun, such use is simply intended todistinguish one item from another, and is not intended to require asequential order unless specifically stated.

In addition, terms of relative position such as “vertical” and“horizontal”, or “front” and “rear”, when used, are intended to berelative to each other and need not be absolute, and only refer to onepossible position of the device associated with those terms depending onthe device's orientation. When this document uses the terms “front,”“rear,” and “sides” to refer to an area of a vehicle, they refer toareas of vehicle with respect to the vehicle's default area of travel.For example, a “front” of an automobile is an area that is closer to thevehicle's headlamps than it is to the vehicle's tail lights, while the“rear” of an automobile is an area that is closer to the vehicle's taillights than it is to the vehicle's headlamps. In addition, the terms“front” and “rear” are not necessarily limited to forward-facing orrear-facing areas but also include side areas that are closer to thefront than the rear, or vice versa, respectively. “Sides” of a vehicleare intended to refer to side-facing sections that are between theforemost and rearmost portions of the vehicle.

1. A method, comprising, by one or more electronic devices: receiving anobservation probability distribution function associated with a targetobject that was detected by one or more sensors of an autonomousvehicle, wherein the one or more sensors are associated with one or moreobject type labels, wherein the observation probability distributionfunction comprises a detection likelihood value associated with each ofthe one or more object type labels; identifying a target attributeassociated with the target object; detecting a target attribute valueassociated with the target attribute; determining a first probabilitydistribution function representing a probability of the autonomousvehicle detecting an object having one or more of the object labelsbased on historical object type detections; determining a secondprobability distribution function defining a probability of theautonomous vehicle detecting the target attribute based on historicalattribute detections; determining a third probability distributionfunction defining a probability of the target attribute actually beingpresent for the target object based on the target attribute value;determining an attribute probability distribution function defining aprobability that the target attribute is actually present for the targetobject based on the first probability distribution function, the secondprobability distribution function, and the third probabilitydistribution function, wherein the attribute probability distributionfunction comprises: a probability value associated with the targetattribute being present for the target object, and a probability valueassociated with the target attribute not being present for the targetobject; and executing one or more vehicle control instructions thatcause the autonomous vehicle to adjust one or more driving operationsusing a future behavior of the target object predicted based on theattribute probability distribution function.
 2. The method of claim 1,wherein determining a first probability distribution function comprises:identifying a prior probability distribution function associated with aset of one or more leaf nodes of a tree structure, wherein the priorprobability distribution is conditioned on observations received until acurrent time; transforming the observation probability distributionfunction to the set of leaf nodes to generate a transformationprobability distribution function; and fusing the transformationprobability distribution function with the prior probabilitydistribution to generate a posterior probability distribution function.3. The method of claim 2, wherein transforming the observationprobability distribution function to the leaf nodes comprises, for eachdetection likelihood value of the observation probability distributionfunction, dividing it equally amongst the one or more leaf nodes thatare its children.
 4. The method of claim 2, wherein fusing thetransformation probability distribution function with the priorprobability distribution to generate a posterior probabilitydistribution function comprises applying a Bayesian filter to thetransformation probability distribution function and the priorprobability distribution.
 5. The method of claim 1, wherein the secondprobability distribution function comprises a probability distributionfunction over an event that the target object is detected as having thetarget attribute, wherein the second probability distribution functionis conditioned on all observations made through a current time.
 6. Themethod of claim 1, wherein determining a third probability distributionfunction representing a probability of the target attribute actuallybeing present for the target object based on the target attribute valuecomprises determining a third probability distribution that comprises: aset of combinations of attributes and object types; and for eachcombination, an associated likelihood value representing a likelihood ofthe object type of the combination having the attribute of thecombination.
 7. The method of claim 1, wherein: determining an attributeprobability value representing a probability that the target attributeis actually present for the target object based on the first probabilityvalue, the second probability value, and the third probability valuecomprises: summing a product value across the target object label andthe target attribute value, wherein the product value comprises aproduct of the first probability distribution function, the secondprobability distribution function, and the third distribution function.8. The method of claim 1, wherein executing one or more vehicle controlinstructions that cause the autonomous vehicle to adjust one or moredriving operations based on the attribute probability distributionfunction comprise: providing data pertaining to the attributeprobability distribution to one or more machine learning models;receiving from the one or more machine learning models informationpertaining to a predicted trajectory of the target object; and adjustingthe one or more driving operations based on the predicted trajectory ofthe target object.
 9. A system comprising: one or more electronicdevices; and a non-transitory computer-readable storage mediumcomprising one or more programming instructions that, when executed,cause one or more of the one or more electronic devices to: receive anobservation probability distribution function associated with a targetobject that was detected by one or more sensors of an autonomousvehicle, wherein the one or more sensors are associated with one or moreobject type labels, wherein the observation probability distributionfunction comprises a detection likelihood value associated with each ofthe one or more object type labels, identify a target attributeassociated with the target object, detect a target attribute valueassociated with the target attribute, determine a first probabilitydistribution function representing a probability of the autonomousvehicle detecting an object having one or more of the one or more objectlabels based on historical object type detections, determine a secondprobability distribution function defining a probability of theautonomous vehicle detecting the target attribute based on historicalattribute detections, determine a third probability distributionfunction defining a probability of the target attribute actually beingpresent for the target object based on the target attribute value,determine an attribute probability distribution function defining aprobability that the target attribute is actually present for the targetobject based on the first probability distribution function, the secondprobability distribution function, and the third probabilitydistribution function, wherein the attribute probability distributionfunction comprises: a probability value associated with the targetattribute being present for the target object, and a probability valueassociated with the target attribute not being present for the targetobject; and execute one or more vehicle control instructions that causethe autonomous vehicle to adjust one or more driving operations using afuture behavior of the target object predicted based on the attributeprobability distribution function.
 10. The system of claim 9, whereinthe one or more programming instructions that, when executed, cause oneor more of the one or more electronic devices to determine a firstprobability distribution function comprise one or more programminginstructions that, when executed, cause one or more of the one or moreelectronic devices to: identify a prior probability distributionfunction associated with a set of one or more leaf nodes of a treestructure, wherein the prior probability distribution is conditioned onobservations received until a current time; transform the observationprobability distribution function to the set of leaf nodes to generate atransformation probability distribution function; and fuse thetransformation probability distribution function with the priorprobability distribution to generate a posterior probabilitydistribution function.
 11. The system of claim 10, wherein the one ormore programming instructions that, when executed, cause one or more ofthe one or more electronic devices to transform the observationprobability distribution function to the leaf nodes comprise one or moreprogramming instructions that, when executed, cause one or more of theone or more electronic devices to, for each detection likelihood valueof the observation probability distribution function, divide it equallyamongst the one or more leaf nodes that are its children.
 12. The systemof claim 10, wherein the one or more programming instructions that, whenexecuted, cause one or more of the one or more electronic devices to usethe transformation probability distribution function with the priorprobability distribution to generate a posterior probabilitydistribution function comprise one or more programming instructionsthat, when executed, cause one or more of the one or more electronicdevices to apply a Bayesian filter to the transformation probabilitydistribution function and the prior probability distribution.
 13. Thesystem of claim 9, wherein the second probability distribution functioncomprises a probability distribution function over an event that thetarget object is detected as having the target attribute, wherein thesecond probability distribution function is conditioned on allobservations made through a current time.
 14. The system of claim 9,wherein the one or more programming instructions that, when executed,cause one or more of the one or more electronic devices to determine athird probability distribution function representing a probability ofthe target attribute actually being present for the target object basedon the target attribute value comprise one or more programminginstructions that, when executed, cause one or more of the one or moreelectronic devices to determine a third probability distribution thatcomprises: a set of combinations of attributes and object types; and foreach combination, an associated likelihood value representing alikelihood of the object type of the combination having the attribute ofthe combination.
 15. The system of claim 9, wherein: the one or moreprogramming instructions that, when executed, cause one or more of theone or more electronic devices to determine an attribute probabilityvalue representing a probability that the target attribute is actuallypresent for the target object based on the first probability value, thesecond probability value, and the third probability value comprise oneor more programming instructions that, when executed, cause one or moreof the one or more electronic devices to: sum a product value across thetarget object label and the target attribute value, wherein the productvalue comprises a product of the first probability distributionfunction, the second probability distribution function, and the thirddistribution function.
 16. The system of claim 9, wherein the one ormore programming instructions that, when executed, cause one or more ofthe one or more electronic devices to execute one or more vehiclecontrol instructions that cause the autonomous vehicle to adjust one ormore driving operations based on the attribute probability distributionfunction comprise one or more programming instructions that, whenexecuted, cause one or more of the one or more electronic devices to:provide data pertaining to the attribute probability distribution to oneor more machine learning models; receive from the one or more machinelearning models information pertaining to a predicted trajectory of thetarget object; and adjust the one or more driving operations based onthe predicted trajectory of the target object.
 17. A non-transitorycomputer-readable medium that stores instructions that is configured to,when executed by at least one computing device, cause the at least onecomputing device to perform operations comprising: receiving anobservation probability distribution function associated with a targetobject that was detected by one or more sensors of an autonomousvehicle, wherein the one or more sensors are associated with one or moreobject type labels, wherein the observation probability distributionfunction comprises a detection likelihood value associated with each ofthe one or more object type labels; identifying a target attributeassociated with the target object; detecting a target attribute valueassociated with the target attribute; determining a first probabilitydistribution function representing a probability of the autonomousvehicle detecting an object having one or more of the object labelsbased on historical object type detections; determining a secondprobability distribution function defining a probability of theautonomous vehicle detecting the target attribute based on historicalattribute detections; determining a third probability distributionfunction defining a probability of the target attribute actually beingpresent for the target object based on the target attribute value;determining an attribute probability distribution function defining aprobability that the target attribute is actually present for the targetobject based on the first probability distribution function, the secondprobability distribution function, and the third probabilitydistribution function, wherein the attribute probability distributionfunction comprises: a probability value associated with the targetattribute being present for the target object, and a probability valueassociated with the target attribute not being present for the targetobject; and executing one or more vehicle control instructions thatcause the autonomous vehicle to adjust one or more driving operationsusing a future behavior of the target object predicted based on theattribute probability distribution function.
 18. The non-transitorycomputer-readable medium of claim 17, wherein determining a firstprobability distribution function comprises: identifying a priorprobability distribution function associated with a set of one or moreleaf nodes of a tree structure, wherein the prior probabilitydistribution is conditioned on observations received until a currenttime; transforming the observation probability distribution function tothe set of leaf nodes to generate a transformation probabilitydistribution function; and fusing the transformation probabilitydistribution function with the prior probability distribution togenerate a posterior probability distribution function.
 19. Thenon-transitory computer-readable medium of claim 17, wherein the secondprobability distribution function comprises a probability distributionfunction over an event that the target object is detected as having thetarget attribute, wherein the second probability distribution functionis conditioned on all observations made through a current time.
 20. Thenon-transitory computer-readable medium of claim 17, wherein determininga third probability distribution function representing a probability ofthe target attribute actually being present for the target object basedon the target attribute value comprises determining a third probabilitydistribution that comprises: a set of combinations of attributes andobject types; and for each combination, an associated likelihood valuerepresenting a likelihood of the object type of the combination havingthe attribute of the combination.